Another aspect that challenges face recognition is the presence of different face poses.
It is known that the performance of neural networks significantly drops when faces are no longer frontal [1]}.
Protocol P from the Multi-PIE dataset is used to observe the performance of neural networks on pose variations.
This protocol provides faces rotated from left to right in steps of 15 degrees of yaw angle, examples can be seen in Fig. fig:multipie-pose-img.
The facial expressions are neutral, without any type of occlusion or strong illumination.
When both eyes are visible, i.e. for \(\pm 45\) , the hand-labeled eye positions are used for alignment.
For the poses with deviations of more than 45 from frontal, only one eye is visible and, therefore, the alignment used the visible eye and the corner of the mouth.
64 identities are used for the dev set and 65 for the eval set, and five frontal images per identity are enrolled in a gallery template.
